Application of Reinforcement Learning on High-Speed Rail Cognitive Radio

Abstract

We all know that wireless communication plays a crucial role in the success of high-speed rail operation. If we apply cognitive radio (CR) technology to individual in high-speed rail, it may raise some problems such as blind learning and frequent channel switch. In this article, we propose to embed the CR technology into base station which called cognitive base station (CBS). Each time, the CBS is qualified to sense all spectrums and choose the best one for individual. The proposed CBS implements reinforcement learning to adapt to environment condition and learn from experience. Finally we prove this model can significantly reduce the frequency hopping.